In today's information technology world, there is an increased interest in processing “big” data to develop insights (e.g., better analytical insight, better customer understanding, etc.) and business advantages (e.g., in enterprise analytics, data management processes, etc.). Customers leave an audit trail or digital log of the interactions, purchases, inquiries, and preferences through online interactions with an organization. Discovering and interpreting audit trails within big data provides a significant advantage to companies looking to realize greater value from the data they capture and manage every day. Structured, semi-structured, and unstructured data points are being generated and captured at an ever-increasing pace, thereby forming big data, which is typically defined in terms of velocity, variety, and volume. Big data is fast-flowing, ever-growing, heterogeneous, and has exceedingly noisy input, and as a result transforming data into signals is critical. As more companies (e.g., airlines, telecommunications companies, financial institutions, etc.) focus on real-world use cases, the demand for continually refreshed signals will continue to increase.
Due to the depth and breadth of available data, data science (and data scientists) is required to transform complex data into simple digestible formats for quick interpretation and understanding. Thus, data science, and in particular, the field of data analytics, focuses on transforming big data into business value (e.g., helping companies anticipate customer behaviors and responses). The current analytic approach to capitalize on big data starts with raw data and ends with intelligence, which is then used to solve a particular business need so that data is ultimately translated into value.
However, a data scientist tasked with a well-defined problem (e.g., rank customers by probability of attrition in the next 90 days) is required to expend a significant amount of effort on tedious manual processes (e.g., aggregating, analyzing, cleansing, preparing, and transforming raw data) in order to begin conducting analytics. In such an approach, significant effort is spent on data preparation (e.g., cleaning, linking, processing), and less is spent on analytics (e.g., business intelligence, visualization, machine learning, model building).
Further, usually the intelligence gathered from the data is not shared across the enterprise (e.g., across use cases, business units, etc.) and is specific to solving a particular use case or business scenario. In this approach, whenever a new use case is presented, an entirely new analytics solution needs to be developed, such that there is no reuse of intelligence across different use cases. Each piece of intelligence that is derived from the data is developed from scratch for each use case that requires it, which often means that it's being recreated multiple times for the same enterprise. There are no natural economies of scale in the process, and there are not enough data scientists to tackle the growing number of business opportunities while relying on such techniques. This can result in inefficiencies and waste, including lengthy use case execution and missed business opportunities.
Currently, to conduct analytics on “big” data, data scientists are often required to develop large quantities of software code. Often, such code is expensive to develop, is highly customized, and is not easily adopted for other uses in the analytics field. Minimizing redundant costs and shortening development cycles requires significantly reducing the amount of time that data scientists spend managing and coordinating raw data. Further, optimizing this work can allow data scientists to improve their effectiveness by honing signals and ultimately improving the foundation that drives faster results and business responsiveness. Thus, there is a need for a system to rapidly develop and deploy analytic code for rapid development and deployment of reusable analytic code for use in computerized data modeling and analysis.